Posted
by
samzenpus
on Wednesday December 30, 2009 @08:45PM
from the nobody-is-right-all-the-time dept.

resistant writes "As the evocative title from Wired magazine implies, Kevin Dunbar of the University of Toronto has taken an in-depth and fascinating look at scientific error, the scientists who cope with it, and sometimes transcend it to find new lines of inquiry. From the article: 'Dunbar came away from his in vivo studies with an unsettling insight: Science is a deeply frustrating pursuit. Although the researchers were mostly using established techniques, more than 50 percent of their data was unexpected. (In some labs, the figure exceeded 75 percent.) "The scientists had these elaborate theories about what was supposed to happen," Dunbar says. "But the results kept contradicting their theories. It wasn't uncommon for someone to spend a month on a project and then just discard all their data because the data didn't make sense."'"

"It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong."

Correct, as long as you take that to mean "experiments in general". It is possible to make mistakes in experiments, and you shouldn't throw out General Relativity because some lab newbie got the setup wrong.

As I said in my other comment [slashdot.org] on this issue, you have to decide which is more likely: that you got the experiment wrong, or the hypothesis is wrong, and this depends on your confidence in both. But you should never hide the result, or else you can get an informational cascade that leads to conformism t

When I first heard about background radiation, I thought to myself, didn't George Gamow predict that in one of his Mr Tompkins books which I read either in high school or junior high school? (and these books were written for juveniles). In fact Gamow did predict it. I read years later in Timothy Ferris's book "The Red Limit, The Search For the Edge of the Universe" that Gamow was astonished that the discoverers of the background radiation did not credit his insight. The book also mentions various scient

If your equipment is malfunctioning, you may end up with data that is fairly random where there should be some pattern or your measurements on your controls don't remotely match the values they should be. As an example, a standardized solution tests for a markedly different concentration than it should; a good sign that something is wrong. Things go wrong occasionally. That is why it is imperative that experiments be repeatable and have good experimental design.

They're especially important when the work is more routine, because that's when you're more tempted to just believe results because they look like what you expect. Or, perhaps you're talking about quality control testing and have a financial interest in having the results pass.

The only way to know that your results are actually right is to have a controlled analytical process. There are lots of ways to do that, and there is a whole field of pursuit around m

And this is what bothers me. If you are willing to run an experiment enough times, you will eventually get data to support your assertions. Get a statistical 90% certainty, and it could be that you ran the scenario 100 times, and throw out the 99 times that did not give you this certainty. The scientific process is bullet proof. The folks who "do science" not necessarily so.

"If you are willing to run an experiment enough times, you will eventually get data to support your assertions."

Yes, I belive Edison tried over 5000 different hand made bulb/filiment combinations before he found one that supported his assertion.

Thowing out data is not about proving pet theories, it's about admitting you cocked up the experiment.
eg: Prof Sumner Miller [abc.net.au] never edited out failed demonstrations from his TV show, nor did he claim the failed demo proved accepted theories of physics were wron

The real secret, Edison found, arguing it out with Charles Batchelor, was to raise the voltage to push a small amount of current through a thin wire to a high-resistance filament. It was an application of the law propounded in 1827 by the German physicist George Ohm, but it was still imperfectly understood. Edison himself said later, "At the time I experimented I did not understand Ohm's law. Moreover, I do not want to understand Ohm's law. It would stop me experimenting." This is Edison in his folksy genius mode. Understanding the relationship linking voltage, current, and resistance was crucial to the development of the incandescent lamp, and he understood it intuitively even if he did not express it in a mathematical formula.

If there's nothing wrong with your equipment or procedures and your experiment fails to find X then it's indicative that your hypothesis is wrong and any group that repeats your experiment is going to know about it immediately.

The scientific process is bullet proof. The folks who "do science" not necessarily so.

What exactly are you advocating?

Maybe he is saying that as long as scientists stand behind science, they'll be fine when the bullets start flying. When they ignore science and try to do their own thing, they get a cap in their asses.

Sure you can get a minority result from an experiment but the less likely the outcome, the more costly it is to get those results and the less likely anyone else who repeats that experiment will see the same result set.

In other words if you tried to rig a test with a 95% confidence level. You would have to run the same test twenty times just to guarantee a result outside your confidence interval *but* that doesn't guarantee that the res

"In September 2004, editors of several prominent medical journals (including the New England Journal of Medicine, The Lancet, Annals of Internal Medicine, and JAMA) announced that they would no longer publish results of drug research sponsored by pharmaceutical companies unless that research was registered in a public database from the start.[11] In this way, negative results should no l

But simply throwing out the data is the 100% wrong thing to do. That destroys the information that would have eventually told you if you were really doing something wrong in your experiment, or if you've discovered something new.

It also creates an information cascade-type situation where everyone culls any non-conf

Often the data is crap, because the measurements are so hard to make.For example, you would think measuring temperature is easy. Not so.Lets say you wish to determine the cooling capacity of an airconditioner.How do you measure the temperature and air velocity gradients across both the return and supply air streams. Do I use 1 sensor, 10 sensors, 100 sensors. Do you create turbulence or laminar flow? How accurate is the humidity measurement?The point is, the data is often crap, because measurements are hard

It can also be that their methodology is wrong, their equipment is wrong, they are simply incompetent.

I suspect that the main thing that differentiates scientists who make a major lasting contribution to science and those that just push knowledge slowly forward is whether they treat such problems as "Oh... that is interesting" or "Crap, must have screwed it up better start it over".

Of course I'm sure those in the first category spend a lot of their time chasing ghosts because most of the time they did in fa

Not religion, but federally funded dogma. More than 20 years ago I became aware of how dogma gets grounded in fundamental research: you need to write grants that fit the dogma. One hapless soul actually stood up during a big AIDS conference and suggested that the researchers were mere lemmings. He, of course, was shouted down, but he was only trying to tell the lemmings to keep an open mind. Fast forward 20+ years and the lemmings are still in control.

Our educational system is totally broken when the educated just want things to fit. Even in mathematics, we're promoting a crop of "just tell me what to do!"

As a grad student in pure mathematics, I'm curious: do you mean in low-level math education, or mathematical research? Basic math education is often just about giving you the tools you need to do your job, so there's nothing wrong with just telling people what to do. Higher-level courses (meaning the kinds only pure-math majors typically take) do require you to actually understand the material and be able to prove things from first principles, probably far more so than any other field.

"It wasn't uncommon for someone to spend a month on a project and then just discard all their data because the data didn't make sense."

That doesn't mean the data is wrong, it means the/hypothesis/ was wrong, if not the theory, and needs to be modified.

If they're really throwing out date just because it 'doesn't make sense', they're doing religion, not science.

a) You've clearly never done any real research or you would be well aware of the hundreds of millions of ways you can screw up an experiment and get nonsense data ( bad machinery, you wired up a detector wrong, the cell lines you were feeding vitamin K happened to get contaminated by bacteria halfway through etc... )

b) There is almost never a clear difference between data and theory. The only raw data you have is a bunch of numbers on a piece of paper, in order to determine if they correspond to your theory or not you need to interpret the numbers somehow, and it may just as well be the interpretation that is wrong as is the theory you were trying to test using the interpreted data.

c) Because you are often restricted by cost and time it's often not feasible to do a full analysis of why your experiment did not work. Hence if you did not get any useful results ( uncertainty was too large, it seems obvious you must have messed up somewhere etc.. ) then frequently the only sane option is to conclude your experiment was a failure.

d) If scientists followed your advice we would never have got the electronic equipment you used to make your post.

Basically your ideas about what science is or should be are extremely naive and to anybody who has done even a high school chemistry experiment it should be clear you have no idea what you're talking about.

Your post is spot on. I'd mod up, but I wanted to clarify (I think you'd agree) that there's a difference between a successful experiment that is inconsistent with a theory and a failed experiment. The purpose of an experiment is not to prove a hypothesis, it's to TEST a hypothesis (or to gather data toward that end). Success means you make a useful statement that aids in the test. Failure means the data were not useful. It has nothing to do with the correctness of the theory or hypothesis.

In the specific quote mentioned, the data "not making sense" doesn't mean that they disagreed with what the experimenter was expecting, it means that they came back in a way that "couldn't happen." That is, that something had gone wrong making the experiment a failure. For example, in some tests I was doing a couple years ago with a prototype radio receiver, I needed to measure its noise level. As a signal, I would sweep a resistive load up and down in temperature---the load outputs noise with intensity that depends on its physical temperature. In this case, as a check, I would start with the load at a low temperature, then heat it past the point of interest, and then cool it back to the starting temperature. I would measure twice, once on the way up and once on the way down. What I found was that the results disagreed between the two measurements. That "does not make sense" in the sense of the article---the testing method was flawed.

In a sense, it was a successful test of a hypothesis. The hypothesis was that the receiver behaves in a particular way (which is what you'd consider the REAL hypothesis under test) AND that the test setup was a valid way to measure that. I disproved the joint hypothesis. In this case, it was the latter part that was invalid---the test was invalid---and I could say nothing about the receiver. This was simply a failed experiment. There is no religion going on by my not claiming that receivers don't behave as we think they do when I just discarded my results.

Every now and then, the reason for a failure might be interesting. This is rare, but when it happens can be responsible for amazing discoveries. In my case, it was a problem of thermal equilibrium. My devices were operating in a vacuum at very low temperatures (about 20 Kelvin) and it can be difficult to affix a heater or a thermometer to just the part of a device that you want to heat or measure....

The OP's statements mirror the general misunderstanding of the scientific method that is rampant in the non-scientific community. We need to help people understand this.

Anyone who has spent time working in a lab knows that not all data is equal. You can get useless results if something isn't quite as clean as necessary, or perhaps you were in a bit of a rush and didn't connect everything perfectly. Any interesting experiment usually has numerous points at which humans can mess things up. Errant data is usually a sign that you have improperly set up the experiment, so you'll spend most of your times reviewing and fixing procedures until you get what you expected.

It is possible to end up with crap data because the premise of your experiment is wrong. You can ignore a variable that should have been controlled or kept equal, or you can measure the wrong variable.

You can also end up with data that neither confirms nor denies your hypothesis, because it allows no statistically significant conclusion.

If the data don't make sense according to your theory, you don't discard the data, you discard the theory and work out a new one that fits the facts as you've observed them. TFA says that Dunbar was watching postdocs doing research, and if so, they should have known better. Alas, too many people who call themselves scientists are more interested in proving their pet theory true than in finding out what's actually going on.

Alas, too many people who call themselves scientists are more interested in proving their pet theory true than in finding out what's actually going on.

It's just a result of how science is performed. Science doesn't have low hanging fruits anymore, consequently any problem that someone investigates takes dedication, because it's intellectually hard or takes lots of effort or both. Most people aren't going to be motivated enough to put that much effort into it without already having an axe to grind, a point

The people who are not motivated enough to put in the effort are not scientists - they are pundits. Researchers who are truly interested in their work - and that would be most of them - put in decades of observation and analysis looking for some truth, because simply grinding an axe would never be personally satisfying. It is lazy and disrespectful of you and other armchair commentators to simply dismiss all that work with a three-line opinion.

As other people have pointed out - sometimes the data is just crap due to the difficulty of making measurements. Sometimes you've measured something other than what you actually need to compare to theory, sometimes there's too much noise.
The skill of a great experimentalist is being able to take good enough data that you can't justify ignoring it if it comes out different to what you expected.

If the data don't make sense according to your theory, you don't discard the data, you discard the theory and work out a new one that fits the facts as you've observed them. TFA says that Dunbar was watching postdocs doing research, and if so, they should have known better. Alas, too many people who call themselves scientists are more interested in proving their pet theory true than in finding out what's actually going on.

This is a beautiful explanation of how science is supposed to work. In reality, science doesn't really work this way. It doesn't work this way in my experience as a scientist, and it doesn't work this way if you read the history of science.

For some good historical examples, see Microbe Hunters, by de Kruif (one of the best science books of all time, although you have to look past the racism in some places -- de Kruif was born in 1890). A good example from physics is the Millikan oil-drop experiment, where he threw out all the data that didn't fit what he was trying to prove -- but then claimed in his paper that he'd never thrown out any data. Galileo described lots of experiments as if he'd done them, even though he didn't actually do them, or they wouldn't have actually come out the way he described.

Michelson and Morley set out to prove the existence of the aether, published their results believing they must be wrong. Nobody else believed them, either. Various people then spent the next 30 years trying to fix the experiment by doing things like taking the apparatus up to the top of a mountain, or doing the experiment in a tent, so that the aether wouldn't be pulled along with the earth or the walls of a building. By the time Einstein published special relativity in 1905, most physicists had either never heard of the MM experiment, or considered it inconclusive.

When your results come out goofy, 99.9% of the time it's because you screwed up. You don't publish it, you go back and fix it. If every scientist published every result he didn't believe himself, the results would be disastrous. If you try over and over again to fix it, and you still fail, only then do you have to make a complicated judgment about whether to publish it or not.

The way science really works is not that scientists are disinterested. Scientists generally have extremely strong opinions that they set out to prove are true using experiments. The motivation is often that scientist A dislikes scientist B and wants to prove him wrong, or something similarly irrational, personal, or emotional. The reason this doesn't cause the downfall of science as an enterprise is that there are checks and balances built in. If A and B are enemies (and if you think the word "enemies" is too strong, you haven't spent much time around academics), and A publishes something, B may decide just to see if he can screw that sonofabitch A over by reproducing his work and finding something wrong with it. It's just like the adversarial system of justice. Society doesn't fall apart just because there are lawyers willing to represent nasty criminals. Einstein was famously asked what he would do if a certain experiment didn't come out consistent with relativity; his reply was that then the experiment would be wrong. Einstein fought against Bohr's quantum mechanics for decades. Bohr fought against Einstein's photons for decades. They were bitter rivals (and also good friends). It didn't matter that they were intensely prejudiced, and wrong 50% of the time; in the end, things sorted themselves out.

If the data don't make sense according to your theory, you don't discard the data, you discard the theory and work out a new one that fits the facts as you've observed them. TFA says that Dunbar was watching postdocs doing research, and if so, they should have known better. Alas, too many people who call themselves scientists are more interested in proving their pet theory true than in finding out what's actually going on.

This is a beautiful explanation of how science is supposed to work. In reality, scien

Feyerabend and many other philosophers of science take a complementary stand to this by stressing the theory-ladenness of "facts."

Yep, they're totally right. For example, this 2003 paper [arxiv.org] claimed to have empirically verified the prediction of general relativity that gravitational forces propagate at the speed of light. The authors made some technical errors, which were rapidly pointed out by others in the field. The final answer is that actually nobody has the faintest clue how to test this specific, centu

If the data don't make sense according to your theory, you don't discard the data, you discard the theory

Not really, assuming the theory is something well-established and tested. Popper oversimplified things - experimental data is rarely so unambigous that you can outright discard a reliable theory. It's much more likely that you messed up than you proved it wrong, or maybe the theory needs a fairly minor modification rather than complete rejection.

If the data don't make sense according to your theory, you don't discard the data, you discard the theory and work out a new one that fits the facts as you've observed them.

If it's a well established theory, you want to eliminate sources of the error before trying to overthrow it. For every Einstein that moves us to the next level from a well established theory there are 3 million cranks that just can't set up a well controlled experiment to save themselves. If you've conducted the experiment sufficiently b

It depends. If most of your data is noise it's fairly worthless anyway and you are better off trying to limit the sources of error and try again.For example consider seismic data. You've got 50Hz or thereabouts induced in the cables near powerlines, you have wind blowing on the geophones, passing cars or trains, differences in soil above the rock and other sources of noise. A lot of seismic data processing seems to be about throwing away the noisy data and stacking up what is left to limit the effect of

Yes, but that pre-supposes the ability to invent a new theory, because scientists are very unwilling to discard a theory if they have no alternative. After all, having no theoretical framework at all is very uncomfortable.

And there I do think that Dunbar makes a perfectly valid point: Any group of specialists who are all of the same mind is very bad a thinking "out of the box" and inventing a new theory. To be able to do that, you need a healthy mixture of different backgrounds, and enough dissent to stimul

If problems occur as you postulate elaborate hypothesis, then stop piling up the elaborate hypothesis! But be sure and still make available your existing (complex) hypothesis, methodology and unexpected data - preventing others from going down the same path with the same methodology is still highly valuable!

Let's say you're looking at a production and consumption cycle involving neurotransmitters and neuroreceptors of some sort, and the various channels of input and output involved. Your starting presumption you base your hypothesis on is that there is a buildup which triggers an electrical signal to stop consumption and clear the channel. The only evidence you can realistically gather for now is protein density at a certain output channel - but others have worked to ensure this is a reliable approach specifically under these circumstances.

So, you do the specific experiment, trigger the signal, but you get a wildly different result - the stop in consumption occurs, but the protein density does not change at all in the output channel. What actually happened is still unknown, only you haven't verified any correlation with your hypothesis. You still have valuable data, but no mechanism to verify under the circumstances. Either your methodology failed, or you misunderstood what was happening - and the world of knowledge is made larger by either... even if your paymasters won't get happy about the result.

Science is often like throwing pebbles in complete darkness - it takes a lot of stones and close listening to make out a mental picture of the scene - especially when there's a lot of noise already around. Everyone would love it if we could just flip the lights on - but we have yet to invent a light that can see into the inner workings of the functioning brain very well. Gotta keep throwing those pebbles for now.

[...] preventing others from going down the same path with the same methodology is still highly valuable!

Exactly. Thomas Edison "discovered" over 5,000 ways how not to create a light bulb. Had he published each and every one of them, perhaps the light bulb would have been invented sooner -- perhaps by someone else, or perhaps by him, collaborating with someone else who had read his published accounts of "how not to create a light bulb."

Is it just me or does this sound like an explanation for some of the Climategate science... But in that case they just massaged or ignored data that didn't agree with their conceptual framework of CO2 causing global warming.

Not that the skeptics are all that immune. They seem to cherry pick data almost as well (just not quite as successfully from the POV of selling their story to the media and political left..)

By "almost as well" I assume you mean "all the time". The "sceptic" arguments are nothing but a parade of cherry picking with little attempt at genuine investigation.

And there's no real evidence of the proper scientists massaging or ignoring anything. Just because a detailed, written account of everything doesn't exist in stolen, incomplete private documents doesn't mean it doesn't exist at all.

And there's no real evidence of the proper scientists massaging or ignoring anything. Just because a detailed, written account of everything doesn't exist in stolen, incomplete private documents doesn't mean it doesn't exist at all.

The behaviour surrounding the data is certainly indicative of a lack of confidence in the findings. Refusing FOI requests and claiming that "the dog ate it" do not show a group filled with the belief that their research is unassailable.

The "sceptic" arguments are nothing but a parade of cherry picking with little attempt at genuine investigation.

Only if you don't actually look around. Richard Lindzen [wsj.com] is a climate researcher at MIT, and has investigated it well (he was one of the authors of the IPCC report). His argument is that there is no strong evidence linking anthropogenic CO2 and a global crisis.

I can't help but think that Neuroscience needs to calm down, sit back, and take a deep breath. We are examining a system and we are trying to reverse engineer it. We can't start out by trying to create elaborate hypothesis for large systems, we need to go low level and examine the simpler systems.
I really think they should hold on to the higher cognitive models for a later time because we can't even completely model C. Elegans and it has the least neurons of any, current, living organism.
The way I see it, I total expect their hypothesis to be wrong, because they don't thoroughly understand the low end of the system.

Why not look at data wherever we can find it? If they find certain patterns that the human brain tends to go through, why not observe them and record them and understand them as well as possible?

It isn't always necessary to know the underlying, simpler systems before we get useful information. I can calculate the resultant change in velocity of two objects after a collision, even though I don't understand the full quantum-mechanical underpinnings of the collision. I know what a computer will do when I ca

As a researcher myself, I certainly hope they don't throw out data too often. There is occasion to do so...sometimes, when trying to establish correlations (admittedly the weakest form of describing a phenomenon, etc), you learn that there is not one. There are times you obtain data that simply says, "These two phenomenon do not strongly affect each other" or "Something we do not know about or have not accounted for is happening all over this mess."

If the data doesn't fit your theory, the problem is most likely neither with the data (which is fine) nor with your theory (which may also be fine) but with the method you used to produce your data. You probably wired in an incorrect resistor, forgot to close a parenthesis in your Perl code, forgot to add the correct amount of EDTA to your reaction, etc. Then your results ended up looking like shit, and not surprisingly. Doing science is hard.

There's no need to postulate any grand conspiracies or take pot-shots at science in general. This paper is examining real people doing real shit. Most of the time we fuck up, and we're not smart enough to figure out where we made the error.

in my experienced - I'm a physical chemist doing atomic resolution condensed phase computer modeling. It's so common that I am troubled when the first analysis gives the answer I expected. I likely spend more time looking for errors when the answer makes sense the first go through. Really.

in my experienced - I'm a physical chemist doing atomic resolution condensed phase computer modeling. It's so common that I am troubled when the first analysis gives the answer I expected. I likely spend more time looking for errors when the answer makes sense the first go through. Really.

This. Getting everything right the first time is like winning the lottery - you don't believe it, and you shouldn't. People doing experiments is a messy thing. Isolating variables is difficult, and much more difficult than just making something happen.

Nobody ever wins the lottery anyway. I took a quick sample of the subset of the population that participated in lotteries and none of them had ever won, and by extrapolating that data I was able to prove that nobody had ever won a lottery ever.

Some other studies have been done that came to different conclusions but I believe that their data collection methodology was flawed as their results didn't agree with mine, so I think they can be safely excluded.

I work with holography. I shine a laser at a piece of film, then develop the film. And presto, I get no image. Do I throw out the theory that exposing film to light should produce an image? No, I assume that I screwed up and go back and start again. It's not uncommon for me to spend 3 months of cleaning, aligning, measuring and so on until I produce a proper image. I then throw away all the "bad" data. Maybe, theoretically, that data could be useful, but there's too many parameters to account for.

How would I remove all the other variables to the point where I can say, with certainty, that there really is something strange going on? It's just not possible. There's a hundred or so components that need to be aligned exactly, be totally clean, work perfectly, etc.

"Dunbar found that most new scientific ideas emerged from lab meetings, those weekly sessions in which people publicly present their data. Interestingly, the most important element of the lab meeting wasn't the presentation -- it was the debate that followed. Dunbar observed that the skeptical (and sometimes heated) questions asked during a group session frequently triggered breakthroughs, as the scientists were forced to reconsider data they'd previously ignored. The new theory was a product of spontaneous conversation, not solitude; a single bracing query was enough to turn scientists into temporary outsiders, able to look anew at their own work."

"I saw this happen all the time," Dunbar says. "A scientist would be trying to describe their approach, and they'd be getting a little defensive, and then they'd get this quizzical look on their face. It was like they'd finally understood what was important."

So that's it: The keys are multiple viewpoints, skepticism, and intellectual competitiveness.

I am calling this neuroscience because it has nothing to do with how the nervous system operates. In this sense I am following the lead of WIRED and/or Dunbar, who can't tell a neuro from a social. From TFA: "Kevin Dunbar is a researcher who studies how scientists study things". OK, he studies things called scientists. scientists are people. The study of people and how they behave is psychology. Science is a social activity. Investigations of social activities are sociology when taken as a whole, or social psychology when considered in terms of the activities of individuals operating within a social group. Dunbar studied social psychology, not neuroscience. There's not a speck of neuroscience cereal in it anywhere. There's very little if any actual social psychology, and psychology, or any science at all. There's talking about science, there's talking to scientists about doing science, and there's watching them do science. There's watching and talking about getting good results and not getting good results, and what people do in the matter case. If Dunbar thinks he's doing neuroscience, I suspect he's not even very clear on science itself, much less the various branches. And it does say he's "a researcher in", not that he's a scientist. I do research in curry recipes from different countries and cultures. I'm a researcher, but not a cultural curriology scientist.

In fact I'll go s far as to say he's a researcher because he knows precious little and is trying to find out basic things, not as is the case with most scientists, someone who knows a fair amount and is trying to build on that with new knowledge. He is apparently not clear on the difference between 'screwing up' and not getting good and/or clean results. This may well be because he was unclear himself as to what it was he was looking at and talking about, and he thought he was just not getting good or clean results, when actually, guess what?

He doesn't let loose any secrets. Anyone can talk to scientists and as what happens if and when things don't turn out as expected. If you get an honest (ie. less concerned with appearances than truth) scientist, anyone would get the same answers. Or one could simply read work from real social psychologists and others who study science and scientists and learn the same things. I myself always recommend Collin's & Pinch's "The Golem" as an illuminating, instructive and entertaining starting point.

And a technical point on methodology: a study that does not find a difference between groups, treatments, whatever, 'fails to reject the null hypothesis' (the assertion that there is no observable difference). It does not prove there is no difference, it merely fails to find one. It fails, but only to find a difference, not to produce a result. It can't say there is no difference, it can only say that it couldn't find one. And, it fails to find a difference, no matter how nicely or hapazardly the data come out. The only studies that "fail" produce no data. Scientists may further fail to find an interpretation, but there's no limitation on trying to figure this out, and it applies to both 'results' (reject null hypothesis) and 'no results' (fail to reject null). Studies that produce data that 'makes no sense' produce data that fails to reject the null. The 'making no sense' is a post hoc evaluation of the data based on an incomplete understanding of the design, collection, analysis or interpretation. Such evaluations are done in science, but they are not part of the scientific process. Therefore when this occurs, it is not a "scientific" result and cannot be taken to reflect in the nature or quality of the work done. If you can't figure what it means, you can't figure out. You cannot say that since you cannot figure it out, then you figure out that it fails. If you think you can take something that 'doesn't make sense' and then say that it makes sense in that it represents a failure, then you've contradicted the assertion that it makes no sense. All you can say is that you don't understand it, and since you d

A. It's not as readable as Mr. Lehrer's article.B. Mr. Lehrer is not the same as Dr. Dunbar, so what a journalist says about a scientist is not what scientist says.C. I gather the discussion of using fMRI on test subjects and noting ACC and DLPFC firing at the same time is, by your analysis, in the realm of cognitive psychology, and not neuroscience.D. So, according to your analysis, neuroscientists use the terms "Cognitive Dissonance" and "Confirmation Bias", while psychologis

Really? Or is it that you are SO politically correct that you cannot see truth.

I happen to have mod points and my on-the-fly ranking went from insightful to interesting to troll and back to interesting.

I've lived long enough to understand that each of the 6 billion people on this earth is different than every other. Some are remarkably good and some are remarkably bad. Most of us are just average in our own interesting ways.

But still, I do believe that genetic differences affect what we are and that gene

"Those who choose to never risk offending anyone are perhaps the most intellectually dishonest among us."

All fine and good, except the OP does not contain anything more intellectual than a bunch of bald assertions wrapped in the emotions of a xenophobe. In other words, you should have modded the GP informative, the OP is a well formed troll.

But still, I do believe that genetic differences affect what we are and that genetic differences can be attributed to where our genes came from.

The theory that race has nothing to do with intelligence has nothing to do with political correctness, and all with science: specifically, the scientific discovery that the taxonomy of human races is not definitive, not specific and has no basis in genetics. Which in turn means that the ggp's assertion that race was a statistically significant factor in their research means that their research was utter crap to begin with.

So let me ask you this then: what makes you think that race is the same as genetics, o

The idea that race is a fiction is a bad, well, fiction, and a clear example of the distortion of thought due to political correctness.

There are a number of human traits (and the genes which cause them) that statistically cluster into groups that correspond to what we consider race. You can test a person's DNA and determine their racial heritage, to a fairly accurate degree. Obviously race is real, if you can nearly automate measuring it. The fact that statistical clusters don't have firm boundaries doesn't mean those clusters don't exist.

Is race relevant? Not for most purposes, but it is for some. I understand that Asians are more likely to have difficulty digesting milk, for example; blacks have a higher tendency to have sickle-cell anemia. Declaring that any test that shows a tendency for races to vary based on genetics is CERTAIN to be flawed because you don't believe race exists is ludicrous.

Here's the problem. If you can't order every single human into one race or another, your model is flawed. If you're forced to resort to mixes of races, well, then you don't have any distinct race left.

Race concepts fall apart once actual taxonomic principles are applied to them. Your examples actually illustrate the problem quite nicely: not nearly all asians have problems with milk - specifically the Japanese the do. Indians (from the Indian subcontinent in Asia) do not. Blacks do not have a higher tendency for sickle-cell anemia, a certain group of people in Africa do. Blacks in the US do not have that trait.

How much does it suck to be so wrong? Your cognitive dissonance must be at a record high.

Here's the problem. If you can't order every single human into one race or another, your model is flawed. If you're forced to resort to mixes of races, well, then you don't have any distinct race left.

Defining race is a classic problem of, well, classification. Put another way, it's like organizing books. Where do you place 'War and Peace'? In the fiction section? In the history section? In the classics section? In the russian literature section? It could legitimately be placed in any of those sections. The problem is that the book has a single physical instance. The book only exists in one place at one time. So, it can only be placed in one category at a time. And this is the problem with any phylogenetic based hierarchical taxonomy. It's not unique to race; it also applies to species, books, weblinks, and any other number of objects. It's why, before search engines, we had all these portal sites, like Yahoo!, who were focused on creating giant taxonomies of weblinks. And it was always a pain, because we had this intuition that a weblink should only exist in a single category at a time. This was a hold-over from library systems, where any particular book can only be placed on a single shelf at a time.

But then we discovered tagging. With tagging, a new type of taxonomy is possible, where a single entity can be placed in multiple categories at a time. And it turns out that tagging is equivalent to a genetic taxonomy. Each tag is equivalent to a gene (or meme, to be more precise). And we now give webpages lists of keywords, which function like a genome of sorts.

So, you're correct that race concepts fall apart at a hierarchical, phylogenetic based taxonomy. But with a genetic based taxonomy, race is 'tagged' by combination of genes... melanin count, lactose sensitivity, sickle-cell anemia, etc.

And what's more, this tagging and clustering, is a precursor to speciation. Consider the following simplified hypothetical example: a) mutant gene (A) interacts with the gene for lactose sensitivity such that, together, they cause a change in sperm mobility due to a lack of calcium, and b) another mutant gene (B) interacts with the gene for sickle-cell anemia such that, together, they cause a change in permeability to an egg due to lack of iron. If these two things were to hypothetically occur, it would make for a situation where sperm and egg couldn't unite, and a lactose intolerant father and sickle-cell anemic mother couldn't have children. Now then, one more consideration: say that these two mutant genes were actually very advantageous. Mutant gene A protects against flu and pnemonia, and mutant gene B codes for sexy pheremones. If these mutant genes are advantageous, then they'll spread throughout the population. But as the mutant genes spread through the population, the carriers of those genes, who also carry the genese for lactose intolerance and/or sickle cell anemia, would lose the ability to breed together. And this would be defined as a speciation event. Not only would those people be of different races, they would be unable to breed together, and would be different species.

In the Unites States, it affects around 72,000 people, most of whose ancestors come from Africa. The disease occurs in about 1 in every 500 African-American births and 1 in every 1000 to 1400 Hispanic-American births. About 2 million Americans, or 1 in 12 African Americans, carry the sickle cell trait.

...making your closing amusingly ironic:

How much does it suck to be so wrong? Your cognitive dissonance must be at a record high.

I'm having difficulty figuring out if you're being serious or not... That just doesn't make any sense.

If a guy of mainly scandinavian ancestry gets a kid with a japanese ancestry, they're not going to pop out a kid who looks like a somalian guy. Political correctness be damned.

Your problem is you're looking for a 'specific gene' that defines ethnicity... It's not. We're dealing with a large set of genes here, where certain collections of patterns predominate in various populations. And these collectively le

So let me ask you this then: what makes you think that race is the same as genetics, or that you can even reliably a race? I mean, outside of some outdated and non-scientific notions of physiognomy and phrenology?

It's simple evolution based on enviromental conditions:Everyone simply evolved so they can best survive in there current environment.

Like the difference between a polar bear and a black bear . It's basically the same bear , that has evolved to suit it's environment better.

"So let me ask you this then: what makes you think that race is the same as genetics,..."

I don't think they are the same, but there is correlation between certain genes and certain groups of people. Whether to classify those groups as races or as extended families is more of a political question than a scientific one.

Just to clarify where I'm coming from, I don't believe that all people of a given skin color should be grouped together as a single race.

I thought World War II empirically proved that the master race is not all its cracked up to be. American mutts and Soviet subhumans kicked the living shit out of the master aryan race. The whole concept of NAZI ideology was that they were the master race, they were not only deserving of victory, but destined, thus, by the most racist rules there are, they proved themselves inferior.

Pics or it isn't true! That said, I'm sure the GP was trolling. But due to the PC movement (more specifically, our backlash to the PC movement) it is too easy to claim to be an authority of something like this and get people to believe their might be a hint of truth to it without him even providing any real details. If you condense the post down to its finer points, it quickly becomes obvious he is bashing people with brown skin, especially by the time you get to the end of the post.

it quickly becomes obvious he is bashing people with brown skin, especially by the time you get to the end of the post.

Without wanting to delve into the finer points of troll detection, that post does have a point: what happens when either the people working on the data have strong opinions about the outcome, or the people giving the funding do?

No quick answers to that one other than to allow the research to kill itself overtime. Since no one but funders/researchers would be aware of any bias, no one could do much about it until they publish their findings. But once they publish, if it is bias then an independent review with repeated testing and evaluation against the original research should either show it to be legitimate or not. If the research was obviously faked, then I hope the "scientists" behind it and any other research they have done are

As soon as I hit submit, I realized I answered the wrong part of this question. I'm expecting them to publish, but what likely trolling AC was saying is they did the research but now armed with their findings they aren't publishing due to possible bias of the funders not wanting to hear that answer. I don't think we can combat this, as once again no one would know about it as it would be an inhouse secret. Unless a researcher on the team decided to go rogue and release it to wikileaks, I think no one would

Or when the results you get aren't acceptable to the people responsible for continued funding.

Years ago, I worked for months trying to reproduce the Polywater research,http://en.wikipedia.org/wiki/Polywaterand eventually reported that I was unable to do so.The department considered my work a failure (as in, I must have been incompetent) and did not publish my findings. When, years later, the publications reporting successful discovery/creation of Polywater were shown to be fraudulent, and my results were correct, I did not even receive an apology.

Throwing out results is unethical as well as irresponsible. Many discoveries have come from re-evaluating what appears to be "bad" data. It might not be possible to use it now, but it should be at least stored.For instance, it has been reported that the "bit of "scruff" on her chart-recorder papers that tracked across the sky with the stars"[1] looked like bad data to Jocelyn Bell Burnell's supervisors. Today we call the phenomenon a pulsar.[1] Wikipedia

Which is why people who claim scientists only care about the truth are wrong. You only cared about the truth and were fired. Plenty of other people would have been looking out for their job first, and made sure their results confirmed what the department expected.

That said- the great thing about science is that eventually the truth will be discovered despite the pressure for money/jobs. It may not happen in a lifetime, but as long as science continues, it will happen.

Are groups of people from very different locations, such as whites and blacks, different? Of course they are! Not so different as to be separate species (yet, and with global communication, maybe never), but evolved in different directions to adapt to different conditions. Just being different means there are activities for which one group will be better suited than the other group. There has to be, otherwise they aren't different, are they? Get over both the racism and the political correctness, and a

No scheme of inequality can be defended as corresponding to natural fact.... Superior and inferior can be determined only with respect to a single quality for a single purpose. Nor can a man's qualities be added together and averaged to give a final score or merit. In short, men are incommensurable and must be deemed equal.- Jacques Barzun

Proving a theory incorrect is often just as valuable as proving a theory correct.

I'd rather say proving a theory incorrect is just as valuable as proving a *hypothesis* correct. If it's a hypothesis, it's no fun proving it wrong (it wasn't established anyway, it might go against your intuition but nobody cares), and if it's a theory, it's no fun proving it right (what are you talking about, of course it's right, we already knew that).